The subject matter discussed in the background section should not be assumed to be prior art merely as a result of its mention in the background section. Similarly, a problem mentioned in the background section or associated with the subject matter of the background section should not be assumed to have been previously recognized in the prior art. The subject matter in the background section merely represents different approaches, which in and of themselves may also correspond to implementations of the claimed technology.
Electrocardiogram (ECG or EKG) is extensively used for determining the condition of a patient's heart. Among many heart conditions, the most lethal and treatable form of myocardial infarctions (i.e. heart attacks), ST-elevation MI, is diagnosed only through accurate analysis of the EKG. Reading the EKG involves two discrete functions: description and interpretation. Description implies identifying individual geometric patterns and defining a suitable terminology. Interpretation involves associating descriptions with anatomical structures in the patient's heart along with physiological and pathophysiological activities, and determining a heart condition responsible for such findings.
Interpreting an exact heart condition of a patient by accurate analysis of the EKG requires years of rigorous training. According to the American College of Cardiology, it requires a minimum of 36 months of training with a suggested 3,500 supervised EKG reads to become an expert in interpreting from the EKGs. The 12-lead EKG detects and records electrical activity of a heart using 12 leads, or sensors. The number of sensors and inherent complexity of concurrent cellular, tissue, and organ-level cardiac electrophysiologic phenomena make describing and interpreting a 12 lead-generated EKG a complex task.
Conventionally-available training to understand the 12-lead EKG suffers from many limitations. These limitations primarily stem from existing methods defying Classic Learning Theory by not overlapping new information with existing knowledge, whereby either learners do not have an opportunity to first identify what they know or are not provided with adequate opportunity to overlap new information with existing information or are not empowered to control the overlap process (sequence, tempo, emphasis and frequency). For example, most commercially available EKG simulators include components only for a menu of electrophysiological rhythm names and a display screen for EKG rhythms, without inclusion of physical replica of the human heart to reflect the corresponding source anatomy and a visual representation of physiologic processes reflecting underlying events. This deficiency takes away a vital opportunity from learners to overlap the new information (e.g., EKG Rhythm) with existing knowledge (e.g., anatomy and physiology), which medical students may spend an entire year learning. Another example of limitations with existing educational models, even those encompassing replica of the heart, is not having a pace control button to control the tempo of the visual input since a heart on average beats at a rate of 80 beats per min and identifying individual waves of a cardiac cycle in tachyarrhythmia becomes difficult due to overlap of waves from overcrowding at higher heart rates.
Thus, in consideration of the above limitations, there remains a need of an interactive learner-controlled technique for improved understanding of the EKG and cardiac activities associated with interpretations made from the EKG.